"Our objective is to educate journalists to use data to see whether their interviews include a representative cross-section of the communities they cover."
- Joe Grimm & Dr. Lucinda Davenport -
"Our objective is to educate journalists to use data to see whether their interviews include a representative cross-section of the communities they cover."
- Joe Grimm & Dr. Lucinda Davenport -
Taking up an Online News Association challenge to “hack the curriculum,” we have been enhancing a core element of JRN 300, a course required of all journalism majors. That element is the reporting that all journalists must do. We have been reiterating a tool to ensure that the people we interview reflect the communities we cover. By hacking the instruction of a skill so central to our program and industry, we are creating sustainable, widespread change.
When journalism does not reflect communities, this is usually unintentional. Accurate coverage requires intentionality. Newsrooms, until now, have simply not had a good way to see in real time whom they are interviewing. By linking several commonly available technologies into a new array, we now can.
The students are developing a tool that aggregates thousands of demographic data points and then organizes, analyzes and makes visualizations of them to show the big picture. Most newsrooms have only a fuzzy picture about the makeup of their communities and no way of knowing whom they are interviewing. Our tool delivers real-time snapshots of how inclusive coverage is by a whole newsroom (or several classes), a work team or a lone journalist.
First, students map their community with Census and election data. Students then gather a little extra data each time they interview someone. Using an app developed by the class this spring, students can enter the data in the field, or have the sources enter their data themselves, assuring them some measure of control and privacy and reducing interviewer bias. Data is immediately uploaded into a database and time-stamped. Just as quickly, that database can generate data visualizations that show whether sourcing matches community demographics. We ask “Is there a correlation?” “Why not?” “Does it matter?” “Should we do something about that?” and “How will we change?” We can then adjust coverage or interviewing choices instantly to later remeasure and compare.
In Spring 2020, three sections of JRN 300, and a graduate-level class that asked to try, are using, developing and understanding the tool. A fourth section of JRN 300, our control group, is not using the tool. We believe our post-test will show an enhanced understanding of community-reflective sourcing among students who have used the technology, seen their performance and thought about it. Our goal is not to develop the tool for better journalism. Our first responsibility is to develop better, more inclusive journalists.
Technology Used
Team Members
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